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Exploration of potential factors affecting the spread of COVID-19 using time series clustering
Topics: Agricultural Geography
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Keywords: Time Series Clustering, COVID-19, Mobility Session Type: Virtual Paper Day: Saturday Session Start / End Time: 4/10/2021 04:40 PM (Pacific Time (US & Canada)) - 4/10/2021 05:55 PM (Pacific Time (US & Canada)) Room: Virtual 9
Authors:
Ziyi Zhang, Department of Electrical and Computer Engineering, Texas A&M University
Diya Li, Department of Geography, Texas A&M University
Zhe Zhang, Department of Geography, Texas A&M University
Nick Duffield, Department of Electrical and Computer Engineering, Texas A&M University
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Abstract
The coronavirus (COVID-19) has spread to more than 135 countries and continues to spread. The virus sickened more than 90,201,652 people until January 2021 and caused 1,937,091 deaths in the world. So far, social distancing plays a vital role in controlling the coronavirus. Governments issue restrictions on traveling, institutions cancel gatherings, and citizens socially distance themselves to limit the spread of the virus. The paper's main focus is to explore changes in people's mobility patterns under the COVID-19 pandemic. Additionally, we conducted a spatial correlation analysis to identify other potential factors that can cause the spread of COVID-19 diseases. The project will help local governments locate the medical facilities and improve the social distancing recommendations regarding the COVID-19 outbreak. For instance, governments can use our models' results to locate risk areas and enforce guidelines to limit interaction in those areas and provide additional medical facilities.
Exploration of potential factors affecting the spread of COVID-19 using time series clustering
